AI Presentation 1
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This document provides biographical information about Şaban Dalaman and summaries of key concepts in artificial intelligence and machine learning. It summarizes Şaban Dalaman's educational and professional background, then discusses Alan Turing's universal machine concept, the 1956 Dartmouth workshop proposal that helped define the field of AI, and definitions of AI, machine learning, deep learning, and data science. It also lists different tribes and algorithms within machine learning.
AI Presentation 1
- 2. 1989 – Graduated from Middle East Technical Unversity 1989-1996 – Turkish Airlines, System Engineer in IT 1997-1998 – Turkish AirForce, Military Duty 1998-2014 – Koçbank/Yapı Kredi Bank, Application Developer/Department Manager in IT 2014-2019 – Freelancer,Master of Science in Data Science
- 4. Alan Mathison Turing The universal machine Computation Theory
- 5. Thinking Computers if they were practically universal, they should be able to do anything. In 1948 he wrote, "The importance of the universal machine is clear. We do not need to have an infinity of different machines doing different jobs. A single one will suffice. The engineering problem of producing various machines for various jobs is replaced by the office work of `programming' the universal machine to do these jobs." Alan M. Turing, Computing Machinery and Intelligence,1950, "Can a machine think?"
- 6. We propose that a 2 month, 10 man study of articial intelligence be carried out during the summer of 1956 at Dartmouth College in Hanover, New Hampshire. The study is to proceed on the basis of the conjecture that every aspect of learning or any other feature of intelligence can in principle be so precisely described that a machine can be made to simulate it. An attempt will be made to nd how to make machines use language, form abstractions and concepts, solve kinds of problems now reserved for humans, and improve themselves. We think that a signicant advance can be made in one or more of these problems if a carefully selected group of scientists work on it together for a summer. . . . For the present purpose the articial intelligence problem is taken to be that of making a machine behave in ways that would be called intelligent if a human were so behaving. Summer Research Project on Articial Intelligence John McCarthy, Claude Shannon and Marvin Minsky, 1957
- 7. Artificial Intelligence is a very broad term. It is an attempt to make computers think like human beings. Any technique, code or algorithm that enables machines to develop, mimic, and demonstrate human cognitive abilities or behaviors falls under this category. Machine learning is the study of algorithms and mathematical models that computer systems use.It is a computer’s ability to learn from a set of data, and adapt itself without being programmed to do so. Deep learning is only a subset of machine learning. It is one of the most popular forms of machine learning algorithms. They use artificial neural networks (ANNs). Data Science is a fairly general term for processes and methods that analyze and manipulate data. It enables artificial intelligence to find meaning and appropriate information from large volumes of data with greater speed and efficiency. Data science makes it possible for us to use data to make key decisions not just in business, but also increasingly in science, technology, and even politics.
- 10. • Symbolists • Connectionists • Evolutionaries • Bayesians • Analogizers Five tribes of AI
- 11. The Master Algorithm an algorithm capable of finding knowledge and generalizing from any kind of data. The algorithm must use paradigms and techniques from each and every tribe
- 13. Artificial intelligence Big data analyticsMachine learning Unsupervised learning Reinforcement learning Supervised learning
- 24. What is Machine Learning (ML)? “A computer program is said to learn from experience E with respect to some task T and some performance measure P, if its performance on T, as measured by P, improves with experience E.”, Tom Mitchell, 1997.
- 25. Why Machine Learning? • It is very hard to write programs that solve problems like recognizing a face. We donʼt know what program to write because we donʼt know how our brain does it. Even if we had a good idea about how to do it, the program might be horrendously complicated. • Instead of writing a program by hand, we collect lots of examples that specify the correct output for a given input. • A machine learning algorithm then takes these examples and produces a program that does the job. If we do it right,the program works for new cases as well as the ones on which we trained it.
- 26. Traditional Programming vs. Machine Learning Data Program Performtask Results Data Model Performtask Results Learning Data Program Requirements Results ML Model
- 27. Machine Learning Workflow Problem Understanding Evaluation Criteria Baseline Prepare Data Train model Error Analysis Simpler model More data / features Production Trained Model Ready to use More complex model Better problem understanding Better baseline
- 29. Ordinary Least Squares Regression (OLSR) Linear Regression Logistic Regression Stepwise Regression Multivariate Adaptive Regression Splines (MARS) Locally Estimated Scatterplot Smoothing (LOESS) Regression Algorithms k-Nearest Neighbor (kNN) Learning Vector Quantization (LVQ) Self-Organizing Map (SOM) Locally Weighted Learning (LWL) Instance-based Algorithms Ridge Regression Least Absolute Shrinkage and Selection Operator (LASSO) Elastic Net Least-Angle Regression (LARS) Regularization Algorithms
- 30. Classification and Regression Tree (CART) Iterative Dichotomiser 3 (ID3) C4.5 and C5.0 (different versions of a powerful approach) Chi-squared Automatic Interaction Detection (CHAID) Decision Stump M5 Conditional Decision Trees Decision Tree Algorithms Naive Bayes Gaussian Naive Bayes Multinomial Naive Bayes Averaged One-Dependence Estimators (AODE) Bayesian Belief Network (BBN) Bayesian Network (BN) Bayesian Algorithms k-Means k-Medians Expectation Maximisation (EM) Hierarchical Clustering Clustering Algorithms
- 31. Perceptron Back-Propagation Hopfield Network Radial Basis Function Network (RBFN) Artificial Neural Network Algorithms Deep Boltzmann Machine (DBM) Deep Belief Networks (DBN) Convolutional Neural Network (CNN) Stacked Auto-Encoders Deep Learning Algorithms Principal Component Analysis (PCA) Principal Component Regression (PCR) Partial Least Squares Regression (PLSR) Linear Discriminant Analysis (LDA) Mixture Discriminant Analysis (MDA) Quadratic Discriminant Analysis (QDA) Flexible Discriminant Analysis (FDA) Dimensionality Reduction Algorithms
- 32. Boosting Bootstrapped Aggregation (Bagging) AdaBoost Stacked Generalization (blending) Gradient Boosting Machines (GBM) Gradient Boosted Regression Trees (GBRT) Random Forest Ensemble Algorithms Computational intelligence (evolutionary algorithms, etc.) Natural Language Processing (NLP) Recommender Systems Reinforcement Learning Graphical Models Other Algorithms
- 33. Process Automation : one of the most common applications of machine learning in finance. The technology allows to replace manual work, automate repetitive tasks, and increase productivity. Security : detecting frauds, identifies suspicious account behavior, financial monitoring, network security Credit scoring : process customer profiles and credit-scoring in real-life environments Algorithmic trading : better trading decisions, analyze thousands of data sources simultaneously Robo-advisory : Portfolio management, Recommendation of financial products
- 34. Deep Learning
- 35. Deep Learning : Biological Inspiration Connected network of neurons. Communicate by electric and chemical signals ~ 1011 neurons ~1000 synapses per neuron Signals come in via dendrites into soma Signal goes out via axon to other neurons through synapses
- 38. Geoffrey Hinton, Yoshua Bengio, Yann LeCun
- 56. GoogleNet
- 69. Challenges in DL
- 79. DL Applications
- 97. Discriminative vs. Generative Models “Cat” Discriminative models Generative models Goal: Learn some underlying hidden structure of the training samples to generate novel samples from same data distribution 9 Goal: Learn a function to map x -> y x y
- 98. Generative Modeling P pdata pmodel 9Slide adapted from Sebastian Nowozin
- 99. Generative Modeling P pdata pmodel Assumptions on P: • tractable sampling Training examples 9Slide adapted from Sebastian Nowozin Model samples
- 100. Generative Modeling P pdata pmodel Assumptions on P: • tractable sampling • tractable likelihood function 1Slide adapted from Sebastian Nowozin
- 101. 1 Why study deep generative models? • Go beyond associating inputs to outputs • Understand high-dimensional, complex probability distributions • Discover the “true” structure of the data • Detect surprising events in the world (anomaly detection) • Missing Data (semi-supervised learning) • Generate models for planning (model-based reinforcement learning)
- 102. Model Examples
- 103. Model to separate noise within a video and single out individual voices in a crowd through deep learning processes
- 104. Video-based emotion recognition using CNN-RNN and C3D hybrid networks
- 105. GoogleNet
- 106. Seq2seq Neural Machine Translation
- 107. Web Traffic Time Series Forecasting
- 115. Natural Language Processing or NLP is the technique that uses algorithmic approaches to help a computer understand and process human language.
- 116. Applications of NLP • Machine Translation • Automatic text summarization • Sentence segmentation • Predicting words • Sentiment analysis • Document classification • Document clustering • Tagging • Paraphrase Detection • Natural language generation • …..and many more.
- 117. Thank you